Centralities of a hypergraph or a memory network

One verb, two estimators, chosen by the class of x:

a hypergraph (net_hg, e.g. from text_hypergraph(), window_hypergraph())

eigenvector-style hypergraph centralities – clique-motif, Z- and H-eigenvector, EDVW PageRank, subhypergraph and Katz; see hg_centrality.net_hg().

a memory network (net_hon, from hon())

PageRank, betweenness and closeness of the higher-order topology, projected onto the first-order states; see hg_centrality.net_hon().

Each method keeps its own arguments; passing an argument that only the other method takes raises hypernets_bad_input.

Usage

hg_centrality(x, ...)

# Default S3 method
hg_centrality(x, ...)

Arguments

x

A net_hg or a net_hon.

...

Arguments of the method for class(x).

Value

A base data.frame, one row per node (or per state), with one column per requested centrality. Any other input raises hypernets_bad_input.

Examples

hg <- text_hypergraph(c(a = "salt and soup", b = "soup and stars"))
hg_centrality(hg, type = "clique")
#>   node    clique
#> 1    a 0.7071068
#> 2    b 0.7071068

seqs <- list(c("a", "b", "c", "a", "b", "c"), c("x", "b", "d", "x", "b", "d"))
hg_centrality(hon(seqs, max_order = 2), type = "pagerank")
#>   state  pagerank
#> 1     a 0.1719145
#> 2     b 0.3222546
#> 3     c 0.1669582
#> 4     d 0.1669582
#> 5     x 0.1719145